BreathMetrics, an open-source computational tool, accurately extracted features from human nasal airflow recordings, intracranial electrophysiological recordings, and computational simulations.
BreathMetrics provides an open-source, automated tool for extracting features from human nasal airflow recordings, enabling advanced research in olfactory and respiratory neuroscience.
Nasal inhalation is the basis of olfactory perception and drives neural activity in olfactory and limbic brain regions. Therefore, our ability to investigate the neural underpinnings of olfaction and respiration can only be as good as our ability to characterize features of respiratory behavior. However, recordings of natural breathing are inherently nonstationary, nonsinusoidal, and idiosyncratic making feature extraction difficult to automate. The absence of a freely available computational tool for characterizing respiratory behavior is a hindrance to many facets of olfactory and respiratory neuroscience. To solve this problem, we developed BreathMetrics, an open-source tool that automatically extracts the full set of features embedded in human nasal airflow recordings. Here, we rigorously validate BreathMetrics' feature estimation accuracy on multiple nasal airflow datasets, intracranial electrophysiological recordings of human olfactory cortex, and computational simulations of breathing signals. We hope this tool will allow researchers to ask new questions about how respiration relates to body, brain, and behavior.
Noto et al. (Wed,) conducted a other in Respiratory behavior and olfaction. BreathMetrics was evaluated on Feature estimation accuracy. BreathMetrics, an open-source computational tool, accurately extracted features from human nasal airflow recordings, intracranial electrophysiological recordings, and computational simulations.
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